retrieval throughput

**Retrieval throughput** is the **rate at which a retrieval system can process queries over time while maintaining acceptable quality and latency** - it reflects capacity planning maturity and infrastructure efficiency. **What Is Retrieval throughput?** - **Definition**: Queries handled per second or per minute by retrieval services. - **Capacity Drivers**: Influenced by index architecture, hardware profile, batching, and concurrency controls. - **Quality Constraint**: High throughput is meaningful only when recall and precision remain stable. - **Operational Role**: Key metric for sizing clusters and handling traffic spikes. **Why Retrieval throughput Matters** - **Scalability Readiness**: Determines ability to support enterprise and consumer load growth. - **Cost Efficiency**: Higher useful throughput lowers per-query infrastructure cost. - **Reliability**: Capacity headroom prevents cascading failures during peak demand. - **Planning Accuracy**: Throughput benchmarks guide shard counts and autoscaling thresholds. - **Service Quality**: Stable throughput protects latency and response consistency. **How It Is Used in Practice** - **Load Testing**: Run realistic mixed-query stress tests and monitor p95 latency under load. - **Horizontal Scaling**: Distribute traffic across shards and replicas with adaptive routing. - **Backpressure Controls**: Use queue limits and priority policies to protect critical workloads. Retrieval throughput is **the practical capacity KPI for retrieval infrastructure** - balanced throughput optimization keeps services fast, reliable, and cost-effective at scale.